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Malicious and Benign Webpages Dataset.

A K Singh1

  • 1Advanced Data Analytics & Parallel Technologies Lab (ADAPT Lab), Department of Computer Science & Information Systems, BITS Pilani, Pilani Campus, India.

Data in Brief
|November 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a large dataset for machine learning-based web security analysis. It aids in detecting malicious websites using extracted features and raw content from 1.5 million webpages.

Keywords:
Deep learningMachine learningMalicious JavaScriptMalicious webpagesWeb security

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Data Science

Background:

  • Web security is a critical challenge due to evolving cyber threats.
  • Machine learning presents a promising approach for identifying malicious websites.
  • Existing datasets may lack the comprehensive features needed for advanced analysis.

Purpose of the Study:

  • To introduce a large-scale dataset for machine learning-based analysis of malicious and benign webpages.
  • To provide a resource supporting both supervised and unsupervised learning for web security.
  • To facilitate research in detecting and mitigating online threats.

Main Methods:

  • Data collection using a specialized web crawler (MalCrawler).
  • Inclusion of extracted attributes and raw webpage content, including JavaScript.
  • Class labeling for supervised learning using Google Safe Browsing API.
  • Dataset size of approximately 1.5 million webpages.

Main Results:

  • A comprehensive dataset suitable for various machine learning techniques, including deep learning.
  • Support for both feature-based and content-based (unstructured data) analysis.
  • Inclusion of code snippets for data extraction and analysis.

Conclusions:

  • The presented dataset is a valuable resource for advancing machine learning applications in web security.
  • It enables robust analysis for detecting malicious websites and improving online safety.
  • The dataset's scale and content diversity support cutting-edge research in cybersecurity.